Lecture
Florence Nightingale Colloquium presents Harrie Oosterhuis
- Date
- Friday 30 April 2021
- Time
- Explanation
- The seminar is targeted at a broad audience, in particular we invite master students, PhD candidates and supervisors interested or involved in the Data Science Research programme as well as colleagues from LIACS and MI to attend. The seminar is organized by the DSO, MI and LIACS.
- Address
- Kaltura Live Room
Optimizing Search and Recommender Systems based on Position-Biased User Interactions
Search and recommendation systems are vital for the accessibility of content on the internet. Search engines allow users to search through large online collections with little effort. Recommendation systems help users discover content that they may not know they find interesting. The basis for these systems are ranking models that turn collections of items into rankings: small ordered lists of items to be displayed to users. Modern ranking models are mostly optimized based on user interactions. Generally, learning from user behavior leads to systems that receive more user engagement than those optimized based on expert judgements. However, user interactions are biased indicators of user preference: often whether something is interacted has less to do with preference and more with where and how it was presented.
In response to this bias problem, recent years have seen the introduction and development of the counterfactual Learning to Rank (LTR) field. This field covers methods that learn from historical user interactions, i.e. click logs, and aim to optimize ranking models w.r.t. the actual user preferences. In order to achieve this goal, counterfactual LTR methods have to correct for the biases that affect clicks.
This talk will go through the main concepts in the counterfactual LTR field and the methods that it uses to estimate bias, and subsequently, to optimize ranking models in a (theoretically) unbiased way.
Join the webinar via Kaltura Live Room
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Register for the Kaltura Live Room link